Wishing you a ball this holiday season — from your friends at the McGovern Institute for Brain Research at MIT!
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Jisong Guan: McGovern Institute 2013 Fall Symposium
Jisong Guan, Tsinghua University
“Chasing the memory traces in mammalian cortex”
On November 4, the McGovern Institute for Brain Research at MIT hosted a joint symposium with the IDG/McGovern Institutes at Beijing Normal University, Peking University, and Tsinghua University. Guest speakers gave talks on subjects ranging from learning and memory to the neurobiology of disease. The symposium was sponsored by the McGovern Institutes and Hugo Shong.
Yichang Jia: McGovern Institute 2013 Fall Symposium
Yichang Jia, Tsinghua University
“Disease mechanisms underlying neurodegeneration caused by RNA metabolism abnormalities”
On November 4, the McGovern Institute for Brain Research at MIT hosted a joint symposium with the IDG/McGovern Institutes at Beijing Normal University, Peking University, and Tsinghua University. Guest speakers gave talks on subjects ranging from learning and memory to the neurobiology of disease. The symposium was sponsored by the McGovern Institutes and Hugo Shong.
Lihong Wang: McGovern Institute 2013 Fall Symposium
Lihong Wang, Tsingua University
“The neural correlates of trait rumination?”
On November 4, the McGovern Institute for Brain Research at MIT hosted a joint symposium with the IDG/McGovern Institutes at Beijing Normal University, Peking University, and Tsinghua University. Guest speakers gave talks on subjects ranging from learning and memory to the neurobiology of disease. The symposium was sponsored by the McGovern Institutes and Hugo Shong.
Yulong Li: McGovern Institute 2013 Fall Symposium
Yulong Li, Peking University
“Spying nerve communication by constructing new genetically-encoded fluorescent probes”
On November 4, the McGovern Institute for Brain Research at MIT hosted a joint symposium with the IDG/McGovern Institutes at Beijing Normal University, Peking University, and Tsinghua University. Guest speakers gave talks on subjects ranging from learning and memory to the neurobiology of disease. The symposium was sponsored by the McGovern Institutes and Hugo Shong.
Kewei Wang: McGovern Institute 2013 Fall Symposium
Kewei Wang
“Targeting voltage-gated Kv7/KCNQ K+ channels for therapeutic potential of neuropsychiatric disorders”
On November 4, the McGovern Institute for Brain Research at MIT hosted a joint symposium with the IDG/McGovern Institutes at Beijing Normal University, Peking University, and Tsinghua University. Guest speakers gave talks on subjects ranging from learning and memory to the neurobiology of disease. The symposium was sponsored by the McGovern Institutes and Hugo Shong.
Liang Li: McGovern Institute 2013 Fall Symposium
Liang Li, Peking University
“Informational masking of speech in people with schizophrenia”
On November 4, the McGovern Institute for Brain Research at MIT hosted a joint symposium with the IDG/McGovern Institutes at Beijing Normal University, Peking University, and Tsinghua University. Guest speakers gave talks on subjects ranging from learning and memory to the neurobiology of disease. The symposium was sponsored by the McGovern Institutes and Hugo Shong.
Speeding up gene discovery
Since the completion of the Human Genome Project, which identified nearly 20,000 protein-coding genes, scientists have been trying to decipher the roles of those genes. A new approach developed at MIT, the Broad Institute, and the Whitehead Institute should speed up the process by allowing researchers to study the entire genome at once.
The new system, known as CRISPR, allows researchers to permanently and selectively delete genes from a cell’s DNA. In two new papers, the researchers showed that they could study all the genes in the genome by deleting a different gene in each of a huge population of cells, then observing which cells proliferated under different conditions.
“With this work, it is now possible to conduct systematic genetic screens in mammalian cells. This will greatly aid efforts to understand the function of both protein-coding genes as well as noncoding genetic elements,” says David Sabatini, a member of the Whitehead Institute, MIT professor of biology, and a senior author of one of the papers, both of which appear in this week’s online edition of Science.
Using this approach, the researchers were able to identify genes that allow melanoma cells to proliferate, as well as genes that confer resistance to certain chemotherapy drugs. Such studies could help scientists develop targeted cancer treatments by revealing the genes that cancer cells depend on to survive.
Feng Zhang, the W.M. Keck Assistant Professor in Biomedical Engineering and senior author of the other Science paper, developed the CRISPR system by exploiting a naturally occurring bacterial protein that recognizes and snips viral DNA. This protein, known as Cas9, is recruited by short RNA molecules called guides, which bind to the DNA to be cut. This DNA-editing complex offers very precise control over which genes are disrupted, by simply changing the sequence of the RNA guide.
“One of the things we’ve realized is that you can easily reprogram these enzymes with a short nucleic-acid chain. This paper takes advantage of that and shows that you can scale that to large numbers and really sample across the whole genome,” says Zhang, who is also a member of MIT’s McGovern Institute for Brain Research and the Broad Institute.
Genome-wide screens
For their new paper, Zhang and colleagues created a library of about 65,000 guide RNA strands that target nearly every known gene. They delivered genes for these guides, along with genes for the CRISPR machinery, to human cells. Each cell took up one of the guides, and the gene targeted by that guide was deleted. If the gene lost was necessary for survival, the cell died.
“This is the first work that really introduces so many mutations in a controlled fashion, which really opens a lot of possibilities in functional genomics,” says Ophir Shalem, a Broad Institute postdoc and one of the lead authors of the Zhang paper, along with Broad Institute postdoc Neville Sanjana.
This approach enabled the researchers to identify genes essential to the survival of two populations of cells: cancer cells and pluripotent stem cells. The researchers also identified genes necessary for melanoma cells to survive treatment with the chemotherapy drug vemurafenib.
In the other paper, led by Sabatini and Eric Lander, the director of the Broad Institute and an MIT professor of biology, the research team targeted a smaller set of about 7,000 genes, but they designed more RNA guide sequences for each gene. The researchers expected that each sequence would block its target gene equally well, but they found that cells with different guides for the same gene had varying survival rates.
“That suggested that there were intrinsic differences between guide RNA sequences that led to differences in their efficiency at cleaving the genomic DNA,” says Tim Wang, an MIT graduate student in biology and lead author of the paper.
From that data, the researchers deduced some rules that appear to govern the efficiency of the CRISPR-Cas9 system. They then used those rules to create an algorithm that can predict the most successful sequences to target a given gene.
“These papers together demonstrate the extraordinary power and versatility of the CRISPR-Cas9 system as a tool for genomewide discovery of the mechanisms underlying mammalian biology,” Lander says. “And we are just at the beginning: We’re still uncovering the capabilities of this system and its many applications.”
Efficient alternative
The researchers say that the CRISPR approach could offer a more efficient and reliable alternative to RNA interference (RNAi), which is currently the most widely used method for studying gene functions. RNAi works by delivering short RNA strands known as shRNA that destroy messenger RNA (mRNA), which carries DNA’s instructions to the rest of the cell.
The drawback to RNAi is that it targets mRNA and not DNA, so it is impossible to get 100 percent elimination of the gene. “CRISPR can completely deplete a given protein in a cell, whereas shRNA will reduce the levels but it will never reach complete depletion,” Zhang says.
Michael Elowitz, a professor of biology, bioengineering, and applied physics at the California Institute of Technology, says the demonstration of the new technique is “an astonishing achievement.”
“Being able to do things on this enormous scale, at high accuracy, is going to revolutionize biology, because for the first time we can start to contemplate the kinds of comprehensive and complex genetic manipulations of cells that are necessary to really understand how complex genetic circuits work,” says Elowitz, who was not involved in the research.
In future studies, the researchers plan to conduct genomewide screens of cells that have become cancerous through the loss of tumor suppressor genes such as BRCA1. If scientists can discover which genes are necessary for those cells to thrive, they may be able to develop drugs that are highly cancer-specific, Wang says.
This strategy could also be used to help find drugs that counterattack tumor cells that have developed resistance to existing chemotherapy drugs, by identifying genes that those cells rely on for survival.
The researchers also hope to use the CRISPR system to study the function of the vast majority of the genome that does not code for proteins. “Only 2 percent of the genome is coding. That’s what these two studies have focused on, that 2 percent, but really there’s that other 98 percent which for a long time has been like dark matter,” Sanjana says.
The research from the Lander/Sabatini group was funded by the National Institutes of Health; the National Human Genome Research Institute; the Broad Institute, and the National Science Foundation. The research from the Zhang group was supported by the NIH Director’s Pioneer Award; the NIH; the Keck, McKnight, Merkin, Vallee, Damon Runyon, Searle Scholars, Klingenstein, and Simon Foundations; Bob Metcalfe; the Klarman Family Foundation; the Simons Center for the Social Brain at MIT; and Jane Pauley.
Even when test scores go up, some cognitive abilities don’t
To evaluate school quality, states require students to take standardized tests; in many cases, passing those tests is necessary to receive a high-school diploma. These high-stakes tests have also been shown to predict students’ future educational attainment and adult employment and income.
Such tests are designed to measure the knowledge and skills that students have acquired in school — what psychologists call “crystallized intelligence.” However, schools whose students have the highest gains on test scores do not produce similar gains in “fluid intelligence” — the ability to analyze abstract problems and think logically — according to a new study from MIT neuroscientists working with education researchers at Harvard University and Brown University.
In a study of nearly 1,400 eighth-graders in the Boston public school system, the researchers found that some schools have successfully raised their students’ scores on the Massachusetts Comprehensive Assessment System (MCAS). However, those schools had almost no effect on students’ performance on tests of fluid intelligence skills, such as working memory capacity, speed of information processing, and ability to solve abstract problems.
“Our original question was this: If you have a school that’s effectively helping kids from lower socioeconomic environments by moving up their scores and improving their chances to go to college, then are those changes accompanied by gains in additional cognitive skills?” says John Gabrieli, the Grover M. Hermann Professor of Health Sciences and Technology, professor of brain and cognitive sciences, and senior author of a forthcoming Psychological Science paper describing the findings.
Instead, the researchers found that educational practices designed to raise knowledge and boost test scores do not improve fluid intelligence. “It doesn’t seem like you get these skills for free in the way that you might hope, despite learning a lot by being a good student,” says Gabrieli, who is also a member of MIT’s McGovern Institute for Brain Research.
Measuring cognition
This study grew out of a larger effort to find measures beyond standardized tests that can predict long-term success for students. “As we started that study, it struck us that there’s been surprisingly little evaluation of different kinds of cognitive abilities and how they relate to educational outcomes,” Gabrieli says.
The data for the Psychological Science study came from students attending traditional, charter, and exam schools in Boston. Some of those schools have had great success improving their students’ MCAS scores — a boost that studies have found also translates to better performance on the SAT and Advanced Placement tests.
The researchers calculated how much of the variation in MCAS scores was due to the school that students attended. For MCAS scores in English, schools accounted for 24 percent of the variation, and they accounted for 34 percent of the math MCAS variation. However, the schools accounted for very little of the variation in fluid cognitive skills — less than 3 percent for all three skills combined.
In one example of a test of fluid reasoning, students were asked to choose which of six pictures completed the missing pieces of a puzzle — a task requiring integration of information such as shape, pattern, and orientation.
“It’s not always clear what dimensions you have to pay attention to get the problem correct. That’s why we call it fluid, because it’s the application of reasoning skills in novel contexts,” says Amy Finn, an MIT postdoc and lead author of the paper.
Even stronger evidence came from a comparison of about 200 students who had entered a lottery for admittance to a handful of Boston’s oversubscribed charter schools, many of which achieve strong improvement in MCAS scores. The researchers found that students who were randomly selected to attend high-performing charter schools did significantly better on the math MCAS than those who were not chosen, but there was no corresponding increase in fluid intelligence scores.
However, the researchers say their study is not about comparing charter schools and district schools. Rather, the study showed that while schools of both types varied in their impact on test scores, they did not vary in their impact on fluid cognitive skills.
“What’s nice about this study is it seems to narrow down the possibilities of what educational interventions are achieving,” says Daniel Willingham, a professor of psychology at the University of Virginia who was not part of the research team. “We’re usually primarily concerned with outcomes in schools, but the underlying mechanisms are also important.”
The researchers plan to continue tracking these students, who are now in 10th grade, to see how their academic performance and other life outcomes evolve. They have also begun to participate in a new study of high school seniors to track how their standardized test scores and cognitive abilities influence their rates of college attendance and graduation.
Implications for education
Gabrieli notes that the study should not be interpreted as critical of schools that are improving their students’ MCAS scores. “It’s valuable to push up the crystallized abilities, because if you can do more math, if you can read a paragraph and answer comprehension questions, all those things are positive,” he says.
He hopes that the findings will encourage educational policymakers to consider adding practices that enhance cognitive skills. Although many studies have shown that students’ fluid cognitive skills predict their academic performance, such skills are seldom explicitly taught.
“Schools can improve crystallized abilities, and now it might be a priority to see if there are some methods for enhancing the fluid ones as well,” Gabrieli says.
Some studies have found that educational programs that focus on improving memory, attention, executive function, and inductive reasoning can boost fluid intelligence, but there is still much disagreement over what programs are consistently effective.
The research was a collaboration with the Center for Education Policy Research at Harvard University, Transforming Education, and Brown University, and was funded by the Bill and Melinda Gates Foundation and the National Institutes of Health.
Brain balances learning new skills, retaining old skills
To learn new motor skills, the brain must be plastic: able to rapidly change the strengths of connections between neurons, forming new patterns that accomplish a particular task. However, if the brain were too plastic, previously learned skills would be lost too easily.
A new computational model developed by MIT neuroscientists explains how the brain maintains the balance between plasticity and stability, and how it can learn very similar tasks without interference between them.
The key, the researchers say, is that neurons are constantly changing their connections with other neurons. However, not all of the changes are functionally relevant — they simply allow the brain to explore many possible ways to execute a certain skill, such as a new tennis stroke.
“Your brain is always trying to find the configurations that balance everything so you can do two tasks, or three tasks, or however many you’re learning,” says Robert Ajemian, a research scientist in MIT’s McGovern Institute for Brain Research and lead author of a paper describing the findings in the Proceedings of the National Academy of Sciences the week of Dec. 9. “There are many ways to solve a task, and you’re exploring all the different ways.”
As the brain explores different solutions, neurons can become specialized for specific tasks, according to this theory.
Noisy circuits
As the brain learns a new motor skill, neurons form circuits that can produce the desired output — a command that will activate the body’s muscles to perform a task such as swinging a tennis racket. Perfection is usually not achieved on the first try, so feedback from each effort helps the brain to find better solutions.
This works well for learning one skill, but complications arise when the brain is trying to learn many different skills at once. Because the same distributed network controls related motor tasks, new modifications to existing patterns can interfere with previously learned skills.
“This is particularly tricky when you’re learning very similar things,” such as two different tennis strokes, says Institute Professor Emilio Bizzi, the paper’s senior author and a member of the McGovern Institute.
In a serial network such as a computer chip, this would be no problem — instructions for each task would be stored in a different location on the chip. However, the brain is not organized like a computer chip. Instead, it is massively parallel and highly connected — each neuron connects to, on average, about 10,000 other neurons.
That connectivity offers an advantage, however, because it allows the brain to test out so many possible solutions to achieve combinations of tasks. The constant changes in these connections, which the researchers call hyperplasticity, is balanced by another inherent trait of neurons — they have a very low signal to noise ratio, meaning that they receive about as much useless information as useful input from their neighbors.
Most models of neural activity don’t include noise, but the MIT team says noise is a critical element of the brain’s learning ability. “Most people don’t want to deal with noise because it’s a nuisance,” Ajemian says. “We set out to try to determine if noise can be used in a beneficial way, and we found that it allows the brain to explore many solutions, but it can only be utilized if the network is hyperplastic.”
This model helps to explain how the brain can learn new things without unlearning previously acquired skills, says Ferdinando Mussa-Ivaldi, a professor of physiology at Northwestern University.
“What the paper shows is that, counterintuitively, if you have neural networks and they have a high level of random noise, that actually helps instead of hindering the stability problem,” says Mussa-Ivaldi, who was not part of the research team.
Without noise, the brain’s hyperplasticity would overwrite existing memories too easily. Conversely, low plasticity would not allow any new skills to be learned, because the tiny changes in connectivity would be drowned out by all of the inherent noise.
The model is supported by anatomical evidence showing that neurons exhibit a great deal of plasticity even when learning is not taking place, as measured by the growth and formation of connections of dendrites — the tiny extensions that neurons use to communicate with each other.
Like riding a bike
The constantly changing connections explain why skills can be forgotten unless they are practiced often, especially if they overlap with other routinely performed tasks.
“That’s why an expert tennis player has to warm up for an hour before a match,” Ajemian says. The warm-up is not for their muscles, instead, the players need to recalibrate the neural networks that control different tennis strokes that are stored in the brain’s motor cortex.
However, skills such as riding a bicycle, which is not very similar to other common skills, are retained more easily. “Once you’ve learned something, if it doesn’t overlap or intersect with other skills, you will forget it but so slowly that it’s essentially permanent,” Ajemian says.
The researchers are now investigating whether this type of model could also explain how the brain forms memories of events, as well as motor skills.
The research was funded by the National Science Foundation.